{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/explicit-interaction-model-towards-text","title":"Explicit Interaction Model towards Text Classification","arxiv_id":"1811.09386","date":"2018-11-23","proceeding":null,"authors":["Cunxiao Du","Zhaozheng Chin","Fuli Feng","Lei Zhu","Tian Gan","Liqiang Nie"],"abstract":"Text classification is one of the fundamental tasks in natural language\nprocessing. Recently, deep neural networks have achieved promising performance\nin the text classification task compared to shallow models. Despite of the\nsignificance of deep models, they ignore the fine-grained (matching signals\nbetween words and classes) classification clues since their classifications\nmainly rely on the text-level representations. To address this problem, we\nintroduce the interaction mechanism to incorporate word-level matching signals\ninto the text classification task. In particular, we design a novel framework,\nEXplicit interAction Model (dubbed as EXAM), equipped with the interaction\nmechanism. We justified the proposed approach on several benchmark datasets\nincluding both multi-label and multi-class text classification tasks. Extensive\nexperimental results demonstrate the superiority of the proposed method. As a\nbyproduct, we have released the codes and parameter settings to facilitate\nother researches.","url_abs":"http://arxiv.org/abs/1811.09386v1","url_pdf":"http://arxiv.org/pdf/1811.09386v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"explicit-interaction-model-towards-text","repo_url":"https://github.com/NonvolatileMemory/AAAI_2019_EXAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-text-classification","task_name":"Multi Class Text Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"model","task_name":"model"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-amazon-review-full","task":"Sentiment Analysis","dataset":"Amazon Review Full","model":"EXAM","rank_in_archive_order":5,"of":9,"metrics":{"Accuracy":"61.9"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-amazon-review-polarity","task":"Sentiment Analysis","dataset":"Amazon Review Polarity","model":"EXAM","rank_in_archive_order":5,"of":9,"metrics":{"Accuracy":"95.5"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-ag-news","task":"Text Classification","dataset":"AG News","model":"EXAM","rank_in_archive_order":10,"of":24,"metrics":{"Error":"7"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-dbpedia","task":"Text Classification","dataset":"DBpedia","model":"EXAM","rank_in_archive_order":12,"of":21,"metrics":{"Error":"1"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-yahoo-answers","task":"Text Classification","dataset":"Yahoo! 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